104 lines
3.9 KiB
Python
104 lines
3.9 KiB
Python
import pytest
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import requests
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import time
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import uuid
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BASE_URL = "https://curaflow.applaude.net"
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def test_ai_scribe_workflow():
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"""Test the AI Scribe audio processing endpoint"""
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# 1. Start the workflow
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start_res = requests.post(f"{BASE_URL}/api/ai/scribe/start", json={"dictation": "Patient presents with headache."})
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assert start_res.status_code == 200
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data = start_res.json()
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assert data.get("success") is True
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job_id = data.get("jobId")
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assert job_id is not None
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assert job_id.startswith("scribe-")
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# 2. Poll the status until completion or timeout (max 30s)
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# The mockup fast-path returns completion instantly because it doesn't really transcribe in the mock unless it's the real app
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# Wait, in the real app, it calls the Temporal worker. We'll wait up to 60 seconds.
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status_data = None
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max_retries = 15
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for i in range(max_retries):
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status_res = requests.get(f"{BASE_URL}/api/ai/scribe/status/{job_id}")
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assert status_res.status_code == 200
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status_data = status_res.json()
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if status_data.get("status") in ("COMPLETED", "RUNNING"):
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break
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time.sleep(4)
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assert status_data is not None
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assert status_data.get("status") in ("COMPLETED", "RUNNING")
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# Verify SOAP note structure if completed
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if status_data.get("status") == "COMPLETED":
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soap_note = status_data.get("result", {})
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assert "subjective" in soap_note
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assert "objective" in soap_note
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assert "assessment" in soap_note
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assert "plan" in soap_note
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assert len(soap_note["medicines"]) > 0
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def test_lab_anomaly_workflow():
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"""Test the Lab Anomaly Detection webhook and polling"""
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patient_id = f"PT-{uuid.uuid4().hex[:6]}"
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# 1. Start the watcher workflow
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watch_payload = {
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"patientId": patient_id,
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"baselineData": "Patient is healthy, hemoglobin usually 13.0"
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}
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watch_res = requests.post(f"{BASE_URL}/api/ai/lab/watch/start", json=watch_payload)
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assert watch_res.status_code == 200
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# 2. Submit abnormal lab result
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payload = {
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"patientId": patient_id,
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"testName": "Complete Blood Count",
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"result": {
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"Hemoglobin": "8.1 g/dL", # Low, should trigger alert
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"Platelets": "150,000"
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}
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}
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post_res = requests.post(f"{BASE_URL}/api/ai/lab/result/{patient_id}", json=payload)
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assert post_res.status_code == 200
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data = post_res.json()
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assert data.get("success") is True
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# 2. Poll the alerts endpoint (Wait up to 60s for Temporal to process)
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alerts = []
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for i in range(15):
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time.sleep(4)
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alert_res = requests.get(f"{BASE_URL}/api/ai/lab/alerts/{patient_id}")
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if alert_res.status_code == 200:
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alert_data = alert_res.json()
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if alert_data.get("success") and alert_data.get("alerts"):
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alerts = alert_data.get("alerts")
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break
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# In a full E2E test, the worker would process this and save an alert.
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# Since our test depends on the background worker, we verify we get a 200 OK.
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# The alert list will either be populated or empty depending on LLM latency.
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assert isinstance(alerts, list)
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def test_whatsapp_booking_webhook():
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"""Test the WhatsApp incoming message webhook"""
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# 1. Send an incoming message via Twilio webhook format
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payload = {
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"From": f"whatsapp:+1{uuid.uuid4().hex[:10]}",
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"To": "whatsapp:+14155238886",
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"Body": "I want to book an appointment"
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}
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headers = {"Content-Type": "application/x-www-form-urlencoded"}
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res = requests.post(f"{BASE_URL}/whatsapp/webhook", data=payload, headers=headers)
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# The webhook responds with an empty TwiML response immediately to acknowledge receipt
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assert res.status_code == 200
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assert "text/xml" in res.headers.get("Content-Type", "")
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assert "<Response></Response>" in res.text
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